scholarly journals Behavioural Economics and Human Decision Making: Instances from the Health Care System

Health Policy ◽  
2020 ◽  
Vol 124 (6) ◽  
pp. 659-664
Author(s):  
Lara Carminati
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Care System ◽  
Behavioural Economics ◽  
Human Decision ◽  
Care System

scholarly journals Unmasking a health care system: the Dutch policy response to the Covid-19 crisis

2021 ◽  
pp. 1-10 ◽  
Author(s):  
Iris Wallenburg ◽  
Jan-Kees Helderman ◽  
Patrick Jeurissen ◽  
Roland Bal
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Care System ◽  
Health Care Systems ◽  
Economic Policies ◽  
Hard Time ◽  
Dutch Health ◽  
Care Systems ◽  
Policy Systems ◽  
Care System

Abstract The Covid-19 pandemic has put policy systems to the test. In this paper, we unmask the institutionalized resilience of the Dutch health care system to pandemic crisis. Building on logics of crisis decision-making and on the notion of ‘tact’, we reveal how the Dutch government initially succeeded in orchestrating collective action through aligning public health purposes and installing socio-economic policies to soften societal impact. However, when the crisis evolved into a more enduring one, a more contested policy arena emerged in which decision-makers had a hard time composing and defending a united decision-making strategy. Measures have become increasingly debated on all policy levels as well as among experts, and conflicts are widely covered in the Dutch media. With the 2021 elections ahead, this means an additional test of the resilience of the Dutch socio-political and health care systems.

scholarly journals Social and Health System Complexities Impacting on Decision-Making for Utilization of Oncology and Palliative Care in an African Context: A Qualitative Study

2019 ◽  
Vol 35 (3) ◽  
pp. 185-191 ◽  
Author(s):  
David A. Agom ◽  
Stuart Allen ◽  
Sarah Neill ◽  
Judith Sixsmith ◽  
Helen Poole ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Palliative Care ◽  
Qualitative Study ◽  
Health Care System ◽  
Health Care Professionals ◽  
Care Delivery ◽  
Semistructured Interview ◽  
Design Data ◽  
Care System

Background: There is a dearth of research focusing on identifying the social complexities impacting on oncology and palliative care (PC), and no study has explored how the health-care system in Nigeria or other African contexts may be influencing utilization of these services. Aim: This study explored how social complexities and the organization of health-care influenced the decision-making process for the utilization of oncology and PC in a Nigerian hospital. Methods: This qualitative study used an interpretive descriptive design. Data were collected using semistructured interview guides with 40 participants, comprising health-care professionals, patients, and their families. Thematic analysis was conducted to generate and analyze patterns within the data. Findings: Three themes were identified: dysfunctional structural organization of the health-care delivery system, service-users’ economic status, and the influence of social networks. The interrelationship between the themes result in patients and their family members decisions either to present late to the hospital, miss their clinical appointments, or not to seek oncological health care and PC. Conclusion: This article offers insights into the role of the health-care system, as organized currently in Nigeria, as “autoinhibitory” and not adequately prepared to address the increasing burden of cancer. We therefore argue that there is a need to restructure the Nigerian health-care system to better meet the needs of patients with cancer and their families as failure to do so will strengthen the existing inequalities, discourage usage, and increase mortality.

scholarly journals Collection and use of EQ-5D for follow-up, decision-making, and quality improvement in health care - the case of the Swedish National Quality Registries

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Olivia Ernstsson ◽  
Mathieu F. Janssen ◽  
Emelie Heintz
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Quality Improvement ◽  
Health Care System ◽  
Swedish Health Care ◽  
National Quality ◽  
Patient Reported ◽  
Different Levels ◽  
Care System

Abstract Background The Swedish National Quality Registries (NQRs) contain individual-level health care data for specific patient populations, or patients receiving specific interventions. Approximately 90% of the 105 Swedish NQRs include any patient-reported outcome measure, with EQ-5D being the most common. As there has been no general overview of EQ-5D data within the NQRs, this study fills a knowledge gap by reporting how the data are collected, presented, and used at different levels of the Swedish health care system. Methods All 46 NQRs with a license for the use of EQ-5D were included. Information was retrieved from the registries’ annual reports or from websites, using a template that was subsequently sent to each registry for completion and confirmation. If considered necessary, the contact was followed-up with an interview, either in-person or over the telephone. The uses of EQ-5D were categorised as denoting usage for follow-up, decision-making, or quality improvement in Swedish health care. Results In total, 41 of the 46 licensed registries reported collection of EQ-5D data. EQ-5D is most commonly collected within registries related to the musculoskeletal system, but it has a wide application also in other disease areas. Thirty-six registries provide EQ-5D results to patients, clinicians, or other decision-makers. Twenty-two of the registries reported that EQ-5D data are being used for follow-up, decision-making or quality improvement. The registries most commonly reported use of data for assessing interventions, and in quality indicators to follow-up the quality of care at a national level. Conclusion Collection and use of EQ-5D data vary across the Swedish NQRs, which may partly be accounted for by the different purposes of the registries. The provided examples of use illustrate how EQ-5D data can inform decisions at different levels of the health care system. However, there is potential for improving the use of EQ-5D data.

Person-Centered Primary Health Care: Now More Than Ever

2015 ◽  
Vol 5 (2) ◽  
pp. 53-59
Author(s):  
Ted Epperly ◽  
Richard Roberts ◽  
Salman Rawaf ◽  
Chris Van Weel ◽  
Robert Phillips ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Decision Making ◽  
Primary Health Care ◽  
Health Outcomes ◽  
Shared Decision Making ◽  
Health Care System ◽  
Shared Decision ◽  
Primary Health ◽  
Care System

 Background: Person-centered primary health care provides first contact care that is comprehensive, continuous, accessible, compassionate, caring, team-based, and above all else person-centered. Primary care by its very nature is integrative in design and process. It connects and coordinates care for the person and uses shared decision making to help value and respect the person’s choices as they navigate through a complex and fragmented health care system.  Objectives: To demonstrate the effectiveness of primary care in achieving the triple aim of better health, better health care, and lower cost. Methods: Critical literature review and evidence based analysis of person-centered primary health care across the world.  Results: Primary care is a systems integrator and improves both the quality of care and the lowering of cost to both people and populations. It has been found that the better a country’s primary care system is, the country will have better overall health care outcomes and lower per capita health care expenditures. Evidence also demonstrates that person-centeredness contributes to higher quality care and better health outcomes. Comprehensiveness of care leads to better health outcomes, lower all-cause mortality, better access to care, less re-hospitalization, fewer consultations with specialists, less use of emergency services, and better detection of adverse effects of medical interventions. The use of the relationship of trust established through primary care health professionals in shared decision making is an effective and efficient means to promote behavior change that results in the triple aim of better health, improved healthcare, and lower costs.  Conclusions: All nations must build a robust and vibrant person-centered primary health care system based on the principles of continuity, comprehensiveness, and person-centeredness. This is important now more than ever to prioritize and rebalance health care systems to address the health care needs of the people that are served. 

scholarly journals Whither economic evaluation in the case of COVID-19: What can the field of mental health economics contribute within the Australian context?

2020 ◽  
Vol 54 (12) ◽  
pp. 1157-1161
Author(s):  
Cathrine Mihalopoulos ◽  
Mary Lou Chatterton ◽  
Lidia Engel ◽  
Long Khanh-Dao Le ◽  
Yong Yi Lee
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Decision Making ◽  
Health Care System ◽  
Disease Outbreaks ◽  
Well Being ◽  
Australian Health ◽  
Life Years ◽  
Mental Health Economics ◽  
Care System

COVID-19 has resulted in broad impacts on the economy and aspects of daily life including our collective mental health and well-being. The Australian health care system already faces limitations in its ability to treat people with mental health diagnoses. Australia has responded to the COVID-19 outbreak by, among other initiatives, providing reimbursement for telehealth services. However, it is unclear if these measures will be enough to manage the psychological distress, depression, anxiety and post-traumatic distress shown to accompany infectious disease outbreaks and economic shocks. Decision making has focused on the physical health ramifications of COVID-19, the avoidance of over-burdening the health care system and saving lives. We propose an alternative framework for decision making that combines life years saved with impacts on quality of life. A framework that simultaneously includes mental health and broader economic impacts into a single decision-making process would facilitate transparent and accountable decision making that can improve the overall welfare of Australian society as we continue to address the considerable challenges that the COVID-19 pandemic is creating.

Primary Care Physician Specialty Referral Decision Making: Patient, Physician, and Health Care System Determinants

2006 ◽  
Vol 26 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Christopher B. Forrest ◽  
Paul A. Nutting ◽  
Sarah von Schrader ◽  
Charles Rohde ◽  
Barbara Starfield
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Decision Making ◽  
Primary Care Physician ◽  
Health Care System ◽  
Care Physician ◽  
Physician Specialty ◽  
Referral Decision ◽  
Care System

scholarly journals Implementing and Evaluating Health as a Service in Fog and Cloud Computing using Raspberry Pi

2020 ◽  
Vol 13 (6) ◽  
pp. 142-155
Author(s):  
Pratik Kanani ◽  
◽  
Mamta Padole ◽  
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Cloud Computing ◽  
Health Care System ◽  
Fog Computing ◽  
Raspberry Pi ◽  
Transmission Delays ◽  
Network Delays ◽  
Qos Parameters ◽  
Care System

Internet of Things (IoT) generates a myriad amount of data, which is sent over the Cloud computing infrastructure for analytics and Business Intelligence. This application scenario suffers network delays, transmission delays and delays in decision making. Due to these drawbacks, the Cloud-based IoT infrastructure is not suitable for time-critical health care applications. To overcome this problem, a smart way is introduced called “Fog Computing” - a LAN based processing approach which has multiple advantages. When IoT, Fog and Cloud Computing are combined, the resultant system’s performance is far better. Hence, the combination results in a very efficient Health Care system. Fog and Cloud Computing have their dimensions that not only support each other but also explore many new application domains. In this paper, the real-time ElectroCardioGram (ECG) based Health Care system is implemented in Cloud and Fog Computing. Different Quality of Service (QoS) parameters like memory consumption, transmission delays, computation delays, network delays, Carbon dioxide emission, data transferred and response time are measured, analyzed and improved to make the system more efficient. Based on the Fog computing characteristics and capabilities, the Raspberry Pi 3 B+ model is configured as a Health Care serving gateway by using different installation and configuration steps. Initially, the proposed system is tested for one patients ECG data analysis over cloud and Fog. In every set up all QoS parameters are measured and later the system is subjected to multiple ECG streams for varying numbers of patients to find the limitations of the Raspberry Pi node as a Fog Computing node. The obtained results show that for more number of ECG streams the Fog node is not able maintain QoS in decision making time. Every QoS parameter is explored in detail for decision-making time. In the end, the Fog computing based proposed system is concluded for its pros and cons and future aspects of the Fog node are discussed to make better systems.

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